Alexandre Jacquillat is the Maurice F. Strong Career Development Associate Professor and Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. His research focuses on data-driven decision-making with applications in air traffic management, urban mobility, and decarbonization. He holds a PhD in Engineering and MS degrees from MIT and École Polytechnique. Education PhD in Engineering, MIT MS in Technology and Policy, MIT MS in Applied Mathematics, École Polytechnique Research Interests His work develops scalable optimization models for efficient, equitable, and sustainable operations. Key areas include stochastic optimization, large-scale systems design, and machine learning applications in transportation and public policy. Recent projects explore vertiport planning for urban aerial mobility and prescriptive analytics for pandemic response. Awards Harold W. Kuhn Award (2024) INFORMS Harvey Greenberg Research Award (2023) MIT Jamieson Prize for Excellence in Teaching (2023) Multiple INFORMS Best Paper Awards (2015-2023) Named Leading Academic Data Leader by Chief Data Officer Magazine (2021-2022) Teaching & Grants Teaches courses in optimization (15.093, 15.083) and analytics (15.072). His grants support work in robotic warehousing, air traffic scheduling, and disaster response logistics. Advises on transportation analytics for industry and government. Labs/Teams Leads MIT Sloan's operations research group, collaborating with industry partners like McKinsey & Co. and Booz Allen Hamilton on transportation analytics and optimization projects.
David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.
Lin Tan is a Professor of Computer Science at Purdue University , holding the Mary J. Elmore New Frontiers Professorship . She joined Purdue in 2019 after serving as a Canada Research Chair and associate professor at the University of Waterloo. She is an ACM Distinguished Member and IEEE Senior Member . Education: PhD in Computer Science, University of Illinois Urbana-Champaign BS in Computer Science and Technology, Zhejiang University Research Interests: Professor Tan’s research lies at the intersection of software engineering , artificial intelligence , and security . Her work focuses on software-AI synergy , software dependability , defect detection & repair , and software text analytics . She leverages machine learning and natural language processing to enhance software reliability, and conversely uses software techniques to improve the dependability of AI systems. Her recent projects include building binary foundation models (Nova), evaluating large language models for code generation and repair, and developing interactive debugging tools that reduce debugging time by one-third. She also explores robot task planning with LLMs and automated front-end development . Awards & Honors: Best Paper Award Finalist, ICRA 2025 ELATES Fellow, 2024-2025 ACM SIGSAC Distinguished Paper Award, CCS 2024 J.P.Morgan AI Faculty Research Awards (2020, 2021, 2022) ACM SIGSOFT Distinguished Paper Awards (ASE 2020, MSR 2018, FSE 2016) Canada Research Chair (2017) Ontario Early Researcher Award (2015) NSERC Discovery Accelerator Supplements Award (2015) Google Faculty Research Awards (2010, 2014) IEEE Micro Top Picks (2006) Advising & Funding: Professor Tan currently advises eight PhD students and has graduated 20+ PhD and Master’s students now thriving in academia (York University, Concordia University, University of Alberta) and industry (Microsoft, Meta, Amazon, Google). Her group is generously supported by NSF , Meta/Facebook Research Awards , J.P.Morgan AI Faculty Awards , and NSF REU programs. Labs & Teams: She leads the Software Reliability & AI Lab at Purdue, recruiting postdocs, PhD, MS, and undergraduate researchers year-round. Lab interests span binary recovery , LLM-based program repair , testing deep-learning libraries , and data-free model extraction .
Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.
Bradley D. Olsen is a full professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research at the intersection of polymer science, soft matter physics, and bioengineering. His work focuses on designing materials for critical applications in biotechnology, hemostasis, and sustainable polymer development while advancing fundamental understanding of polymer network mechanics and self-assembly. Education: Ph.D. in Chemical Engineering, University of California Berkeley (2007) S.B. in Chemical Engineering, Massachusetts Institute of Technology (2003) Olsen's research spans protein-based materials, block copolymer phase behavior, and mechanochemical hydrogels. He has pioneered methods for quantifying polymer network topology, developing hemostatic nanoparticles, and creating bio-inspired materials for selective biomolecular transport and medical applications. His recent publications emphasize data-driven approaches to polymer characterization and educational outreach in materials science. Scientific Awards: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters Young Investigator Award (2021) MIT Committed to Caring Honor (2019) AIChE Owens Corning Early Career Award (2019) APS Dillon Medal (2018) Kavli Emerging Leader in Chemistry (2017) ACS Polymer Division Fellow (2016) Camille Dreyfus-Teacher Scholar (2015) Alfred P. Sloan Research Fellow (2014) NSF Career Grant (2013) NIH Postdoctoral Fellowship (2008-2009) Hertz Fellow (2003-2007) Barry M. Goldwater Scholarship (2002) Olsen has received significant grant support including NSF Career (2013) and AFOSR (2012) awards. His teaching activities include innovative international outreach like the 2025 soccer-themed science camp in Brazil. The Olsen Group at MIT explores advanced materials with applications ranging from trauma care to sustainable polymers.
Vikramaditya G. Yadav is an Associate Professor at the University of British Columbia (UBC) in the Department of Chemical and Biological Engineering, Faculty of Applied Science. He directs the Master of Engineering Leadership (MEL) Program in Sustainable Process Engineering and leads the BioFoundry research group. Education: B.A.Sc., University of Waterloo (2007) Ph.D., Massachusetts Institute of Technology (2013) Postdoctoral Associate, Harvard University (2014) His research spans sustainable chemical manufacturing, metabolic engineering, and biotechnology. Key areas include: Designing biosynthetic enzymes for biomass valorization Developing bioremediation strategies for industrial water quality Creating innovative drug delivery systems and tissue engineering solutions Advancing synthetic biology for pharmaceutical and bioenergy applications His recent work focuses on ocular drug delivery, cannabinoid biosynthesis in E. coli, lignin-based nanoparticles for cancer therapy, and computational analysis of plant secondary metabolites. Collaborations with start-ups, industry, and medical labs drive innovation in Canada's bioeconomy. Professional Leadership: Chair, Biotechnology Division of the Chemical Institute of Canada Associate Editor, The Canadian Journal of Chemical Engineering He is affiliated with UBC's BioProducts Institute and contributes to project-based learning pedagogy.
Michael J. Aziz is the Gene and Tracy Sykes Professor of Materials and Energy Technologies at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He serves as Area Chair for Materials Science and Mechanical Engineering and is a Faculty Associate at the Harvard University Center for the Environment. His research focuses on electrochemical engineering for energy and environmental applications, including redox flow batteries, carbon capture, and sustainable energy technologies. Aziz leads the Aziz Group, which develops grid-scale energy storage solutions and innovative methods for CO₂ removal. He holds equity in Quino Energy, a startup commercializing his battery research, and serves as Chief Scientist and Board Member. His work bridges fundamental materials science with practical engineering, emphasizing ClimateTech solutions. Key contributions include aqueous organic redox flow batteries, quinone-based carbon capture systems, and wearable energy storage devices. Education & Affiliations: Affiliated with SEAS since joining Harvard, his academic roles include coordinating the Graduate Consortium for Energy and Environment (2009–2018). His lab (Materials Science Group) is located at McKay 504, with administrative support from Sabrina Azinheira. Research Interests: Aziz's group investigates electrochemical energy storage, CO₂ capture via electrochemical systems, and novel materials for sustainable technologies. They employ advanced techniques like operando electrochemical fluorescence microscopy to study porous electrode dynamics and battery degradation mechanisms. Their work emphasizes scalability and real-world applicability, such as grid-scale battery infrastructure and decarbonization strategies. Recent Trends in Publications: Aziz's recent work emphasizes carbon capture innovations (e.g., acid-base concentration swing methods), hydrogen storage under ambient conditions, and electrochemical synthesis of industrial chemicals like hydrogen peroxide. His group also develops open-source tools like RFBzero for battery modeling and explores bioinspired materials (e.g., self-gelling hydrogel batteries). Awards & Recognition: While no personal awards are explicitly listed in the text, his team members (e.g., Dawei Xi) have received accolades such as the 2025 Carbon Future Young Investigator Award. Aziz's contributions have been recognized through industry partnerships and startup ventures. Advising & Industry Impact: Aziz advises PhD students focusing on electrochemical systems (e.g., Jordan Sosa, Tommy George). His industry engagement includes licensing intellectual property to Quino Energy, which achieved a manufacturing milestone in 2024 for grid-scale battery systems. His research bridges academia and industry, addressing climate challenges through technological innovation. Labs & Teams: The Aziz Group includes interdisciplinary researchers from electrochemistry, chemical engineering, and materials science. Collaborators include institutions like MIT and industry partners. Current projects target next-gen batteries, CO₂ removal systems, and scalable energy storage solutions.
Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.
Catherine Butler is a Senior Fellow of the Higher Education Academy and Programme Director of the DClinPsy Programme at the University of Bath's Department of Psychology. She holds external examiner roles at Cardiff University, University College London, and others. Her academic journey includes a Doctor of Clinical Psychology from the University of East London, an MBA from the Open University, and advanced training in Systemic Supervision at the University of Exeter. Her research focuses on Inclusion, Whiteness, Gender and Sexual Minorities, Intersectionality, and Systemic Therapy, with contributions to anti-racism and climate crisis studies. Key projects include exploring systemic therapy supervision, validation of therapeutic scales, and shared learning courses with clinical psychology trainees. Dr. Butler’s scholarly work addresses topics such as gender dysphoria in neurodivergent populations, relapse experiences in addiction therapy, and systemic training in clinical psychology programs. She has been recognized for her contributions through awards like the Senior Fellow of the Higher Education Academy. Her professional activities include roles on the British Psychological Society committee, conference presentations, and advisory roles in academic and clinical settings. She has supervised 18 doctoral students and led projects funded by UK charities such as the Association for Family Therapy.
Hugo de Lasa is a Full Professor at the Department of Chemical and Biochemical Engineering, Faculty of Engineering, University of Western Ontario. He holds a Bachelor in Chemical Engineering (1968) from Universidad Nacional del Sur, Argentina, and a Doctoral degree (1971) from Université de Nancy, France. Research Focus: Catalysis, Photocatalysis, Chemical Reactor Engineering, Fluidization, Biomass Gasification Awards: Research Excellence Prize (1998), Fellow of the Chemical Institute of Canada (2000), Medal of Research and Development (2000), Doctor Honoris Causa (2004, 2018) His work spans chemical reactor design , photocatalytic hydrogen production , and fluidized bed technologies . Recent publications highlight machine learning applications in chemical equilibrium modeling and CO2 capture using microalgae. He founded the Chemical Reactor Engineering Centre (CREC) and Recat Technologies Inc. , a university spin-off commercializing reactor innovations. Awards include the Vanguard Award (2019) and Commemorative Issue in Catalysts Journal (2020). His research has generated 389 peer-reviewed publications , 14 patents , and over 10,000 citations .
Anders Damgaard is an Associate Professor and Head of BSc studies at the Department of Environmental and Resource Engineering (DTU Sustain), Technical University of Denmark. His research focuses on environmental assessment methodologies, particularly Life Cycle Assessment (LCA), and their application to waste management systems, resource recovery, and policy development. He leads the development of LCA models like EASEWASTE/EASETECH and collaborates with institutions such as the Danish EPA and Nordic Council of Ministers. Key research areas include carbon footprints of waste treatment, textile waste composition, and additive impacts in plastics recycling. Education: Not explicitly stated in provided texts. Research Interests: Waste management optimization, circular economy frameworks, sustainable technologies, and policy-driven environmental assessments. His recent publications address topics such as carbon footprints of sewage sludge treatments, Nordic textile waste composition, and challenges in plastic additive inclusion in LCA studies. He supervises PhD students in projects involving lifecycle modeling, construction waste recycling, and consumer practices in circular economies. As part of DTU Sustain, he contributes to interdisciplinary teams advancing sustainable resource management and policy solutions.
Tengyu Ma is an Assistant Professor of Computer Science at Stanford University. His research focuses on machine learning, deep learning, optimization, and theoretical computer science. He is particularly known for work on neural networks, reinforcement learning, and algorithmic guarantees in AI systems. His email is tengyuma@stanford.edu . Ma's research interests span foundational aspects of machine learning, including generalization theory, optimization algorithms, and the theoretical underpinnings of deep learning. He has contributed to areas such as self-play theorem provers, learning rate schedules, and robustness in low-light vision tasks. His work often bridges theoretical insights with practical algorithm design. His recent publications emphasize advancements in large language models (LLMs), theorem proving via self-play, and understanding training dynamics in deep networks. Despite prolific output, no specific scientific awards are explicitly mentioned in the provided texts. Ongoing work includes exploring in-context learning mechanisms, formal verification of AI systems, and efficient pretraining techniques. His research has implications for both theoretical understanding and real-world applications of AI.
Marek Locmelis is an Associate Professor at the Department of Earth and Planetary Sciences and the Bureau of Economic Geology within the Jackson School of Geosciences at the University of Texas at Austin. His research focuses on magmatic, hydrothermal, and sedimentary ore deposits, with an emphasis on critical mineral supply strategies, including recycling of mine waste and STEM education in economic geology. He holds a PhD from Macquarie University and prior roles at Missouri University of Science and Technology and NASA Goddard Space Flight Center. Education PhD in Earth and Planetary Sciences, Macquarie University (Australia) Diploma (MSc equivalent) and Pre-Diploma (BSc equivalent) in Geosciences, University of Hannover (Germany) Research Interests His work spans geochemistry, petrology, and planetary evolution, with a focus on critical minerals (e.g., lithium, rare earth elements) and novel exploration techniques. He investigates pathways to enhance domestic critical mineral recovery through reprocessing mine waste and optimizing production streams. His planetary research includes atmospheric toxicity studies and habitability potential of extraterrestrial environments. Awards Fellow of the Society of Economic Geology (SEG) SEG Graduate Student Fellowship Program Chair NSF CAREER Award (2020) NASA Postdoctoral Fellowship (2014) Advising & Grants Locmelis has advised postdocs and students through workshops on critical mineral resilience. His NSF CAREER project explores metal transport via magmatic-hydrothermal fluids. He co-organized conferences on critical minerals and led the Roadmaps Into the Geosciences (RIGS) program to support student career development. Labs & Teams He collaborates with the Bureau of Economic Geology and interdisciplinary teams in critical mineral research, combining fieldwork, geochemical analysis, and policy advocacy.
Min Hu is an Assistant Professor in the Department of Economics, Philosophy, and Political Science at the University of British Columbia, Okanagan Campus. He holds an adjunct position at Dalhousie University's School of Health Administration. His primary research focuses on Health Economics, Applied Microeconomics, and Labour Economics, with an emphasis on vulnerable populations in Canada, Indigenous health, income inequalities, and cancer economics. He teaches courses in Microeconomics, Health Economics, and Statistics. Education: PhD in Economics (2019), Dalhousie University; Postdoctoral Fellowship in Health Economics (2019–2020), Dalhousie University. Previous degrees include an M.A. (2014–2016) and B.A. in Economics from Dalhousie University. Research interests include the socio-economic determinants of health disparities, particularly among Indigenous peoples and immigrants, and the economic impacts of cancer-related policies. He employs applied econometric methods to analyze labor market outcomes and social policy effectiveness. His recent articles explore sustainable biomass utilization, energy policies, and environmental engineering innovations. Key themes include biomass supply chain optimization, CO₂ reduction strategies, and waste-to-energy technologies. Scientific Awards: Recipient of SSHRC New Frontiers Grant (2023–2026), CIHR Team Grant (2024–2026), and multiple UBC startup grants. His work is supported by SSHRC, CRDCN, and Indigenous Services Canada. He advises students in Economics and Global Studies (IGS) programs. Grants and funding span health economics, Indigenous well-being, and sustainable energy systems. Collaborations include interdisciplinary teams in health administration and environmental policy. Labs/Teams: Engaged with UBC's Okanagan Sustainability Initiatives and Dalhousie's Health Economics Research Group. Affiliated with professional organizations like the American Economic Association and Canadian Health Economics Study Group.
Stephen Billings is an Associate Professor of Finance at the Leeds School of Business, University of Colorado Boulder, where he holds the Global Real Estate Capital Markets Chair position. He serves as the faculty director of the MS in Real Estate program and research director for the CU Real Estate Center. His work spans multiple research centers including the Center for Research on Consumer Financial Decision Making and the Michael A. Klump Center for Real Estate. Billings received his PhD in Economics from the University of Colorado, a MS in urban planning from the University of North Carolina, and a BS in economics from Georgetown University. Prior to joining CU Boulder in 2016, he was faculty at the University of North Carolina Charlotte with appointments in both economics and public policy, where he also served as faculty director for the real estate center. His research focuses on the intersection of housing, schools, neighborhoods and crime, addressing critical policy issues including the impact of public investment in light-rail transit on neighborhoods, school segregation's influence on academic achievement and crime, neighborhood gentrification causes, lead-based paint in low-income housing, and the effects of sea level rise and natural disasters on neighborhood sorting and household finances. Billings employs sophisticated analysis of large micro-datasets with studies based on data and policies in Charlotte, NC and Denver, CO. His work connects urban economics with public policy, demonstrating how neighborhood environments shape individual outcomes across multiple dimensions. Billings' recent publication record demonstrates a consistent focus on neighborhood effects, school choice impacts, crime economics, and housing market dynamics. His work frequently examines the intersection of race, housing, and economic outcomes, with particular attention to policy implications. He has developed innovative methodological approaches to studying residential amenities and neighborhood spillovers, with increasing attention to climate change impacts on housing markets. His research appears in top journals including The Quarterly Journal of Economics, American Economic Review: Insights, and Journal of Urban Economics. As an educator, Billings teaches undergraduate and masters classes in real estate finance and economics, as well as doctoral and masters level courses on cost-benefit analysis and statistical methods. He has extensive consulting experience conducting economic impact studies, including assessments of North Carolina's public higher education system, affordable housing needs in Colorado communities, and the economic effects of marijuana legalization on property markets. His practical policy work complements his academic research, creating a bridge between theoretical insights and real-world applications. Billings leads significant research initiatives through the CU Real Estate Center, where he directs studies examining the impact of business improvement districts and other urban development policies. His work bridges academic research with practical policy applications, making substantial contributions to our understanding of how housing, education, and neighborhood environments shape individual outcomes and community development. His December 2023 memo on the HUD Officer Next Door Program exemplifies his engagement with current housing policy issues.
